用适配器让蛋白语言模型按功能生成新蛋白，还能泛化到未见过的功能。
Function-Guided Conditional Generation Using Protein Language Models with Adapters
- 用适配器微调蛋白语言模型，支持酶家族、分类、自然语言等多种功能条件。
- 在已知功能上性能媲美或超越现有方法，且能生成罕见和未见功能的蛋白。
- 方法灵活高效，适合扩展到其他生成语言模型，科研与药物设计者可重点关注。
基于蛋白质语言模型（PLMs）的提示方法可实现目标功能的蛋白质条件生成，如特定酶家族。但这类方法仅支持简单的分词条件，且无法泛化至未见功能。本文提出ProCALM（Protein Conditionally Adapted Language Model），通过适配器对ProGen2进行微调，实现基于多种蛋白功能表示（如酶家族、分类、自然语言描述）的条件生成。ProCALM在目标功能序列生成上达到或超过现有方法性能，且具备生成稀有及未见功能蛋白的能力。整体方法灵活高效，可拓展至多种生成语言模型。
原文摘要 · Abstract (English)
The conditional generation of proteins with desired functions is a key goal for generative models. Existing methods based on prompting of protein language models (PLMs) can generate proteins conditioned on a target functionality, such as a desired enzyme family. However, these methods are limited to simple, tokenized conditioning and have not been shown to generalize to unseen functions. In this study, we propose ProCALM (Protein Conditionally Adapted Language Model), an approach for the conditional generation of proteins using adapters to PLMs. While previous methods have used adapters for structure-conditioned generation from PLMs, our implementation of ProCALM involves finetuning ProGen2 to condition generation based on versatile representations of protein function-e.g. enzyme family, taxonomy, or natural language descriptions. ProCALM matches or exceeds the performance of existing methods at conditional sequence generation from target functions. Impressively, it can also generalize to rare and unseen functions. Overall, ProCALM is a flexible and computationally efficient approach, and we expect that it can be extended to a wide range of generative language models.
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